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Development and parameter identification of state space mathematical models simulating the inhibition of tumors under one anticancer agent

Sgourou Maria

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Summary

Nowadays, scientists are contacting an extensive research regarding the earlydiagnosis and the effective treatment of cancer. Key role in the above medicalprocedures plays their non invasive nature and how fast they can be addressed. To achieve these goals scientists use originally mathematics combined with computer science and control theory, along with other fields that will not be discussed in this diploma thesis. Using the above scientific fields, this diploma thesis aims to extract some useful results in order to accelerate the research about anticancer agents. The goal is to speed up the clinical phase by predicting the tumors’ behaviοur using mathematical models. By doing so the drugs can be released faster and be tested with less invasive procedures. In addition, the companies will spend less money in the research, which may affect positively the price of the drug. Starting by presenting some information about cancer and tumors in general, and then presenting A Glycolysis Based in-silico Model for the Solid Tumor Growth by professor M. E. Zervaki, this diploma thesis focuses on the Tumor Growth Inhibition (TGI) model by professor Monica Simeoni and her team. By using M. J. Box’s algorithm, TGI model’s parameters are calculated for fourteen case studies. In six of those case studies mice received treatment and in the rest case studies they remained untreated. The secondary parameters of the model for the fourteen cases are calculated as well. Last, but not least, the equilibrium points for four cases of treated animals are calculated. Then the results are compared with the theoretical predictions and discussed.

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